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Forecast of Sugarcane Yield in Chongzuo, Guangxi-LSTM Model Based on Fusion of Trend Yield and Meteorological Yield

文献类型： 外文期刊

作者： Ma, Pengcheng 1 ; Zhang, Na 1 ; Yang, Yunhai 1 ; Wang, Zeping 3 ; Li, Guodong 1 ; Fu, Zhishan 1 ;

作者机构： 1.Guilin Univ Elect Technol, Sch Math & Comp Sci, Guilin 541004, Peoples R China

2.Ctr Appl Math Guangxi GUET, Guilin 541004, Peoples R China

3.Guangxi Acad Agr Sci, Nanning 530007, Peoples R China

关键词： fusion prediction model; LSTM; trend yield; meteorological yield; forecast of sugarcane yield

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

ISSN：

年卷期： 2024 年 14 卷 11 期

页码：

收录情况： SCI

摘要： Purpose: This paper develops a high-precision yield fusion prediction model for the sugarcane industry in Chongzuo, Guangxi, based on the trend yield and meteorological yield using the long short-term memory (LSTM) model to cope with the multiple factors affecting sugarcane production. Decision support is provided to agricultural producers, policymakers, and supply chain managers so that they can plan resource allocation, market strategies, and policy directions more effectively. Methods: The paper modeled trend yield and weather yield separately to explore the complex relationship between the two in influencing sugarcane production. Trend yields were predicted using the exponential smoothing and multilayer perceptron (MLP) models, while meteorological yields were modeled using stepwise regression. The predicted yields were used again as input variables into the LSTM deep learning network to fit the nonlinear relationship between the two yields. Results: The results showed that (1) the fusion strategy of meteorological yield and MLP trend yield adopted by the model was superior to the fusion strategy of meteorological yield and exponentially smoothed trend yield, achieving a very low mean square error (MSE) of 0.011 and a goodness of fit as high as 0.979, which indicated that the model prediction was highly in agreement with the actual yield, confirming the validity of the method. (2) The prediction curve is basically consistent with the trend of actual sugarcane yield, which predicts that the sugarcane yield in Chongzuo, Guangxi, is expected to maintain a stable and small growth trend in the next eight years. (3) The fusion prediction model proposed in this study provides an accurate and practical solution for sugarcane yield prediction in Chongzuo, Guangxi, with the unique advantage of effectively analyzing and integrating the natural and socio-economic factors affecting the yield, which is of significant reference value for the prediction of sugarcane yield in the local area and even in similar ecoregions.

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